Interpreto: Demystifying Transformers with Explainability
Research#Transformers🔬 Research|Analyzed: Jan 10, 2026 12:18•
Published: Dec 10, 2025 15:12
•1 min read
•ArXivAnalysis
This article introduces Interpreto, a library designed to improve the explainability of Transformer models. The development of such libraries is crucial for building trust and understanding in AI, especially as transformer-based models become more prevalent.
Key Takeaways
- •Interpreto aims to provide insights into how transformer models make decisions.
- •The library likely offers various methods for visualizing and interpreting model behavior.
- •Increased explainability can facilitate debugging and improve model reliability.
Reference / Citation
View Original"Interpreto is an explainability library for transformers."